Methyl-hydroxylation and subsequent oxidation to produce carboxylic acid is the major metabolic pathway of tolbutamide in chimeric TK-NOG mice transplanted with human hepatocytes

Abstract

1. Tolbutamide is an oral anti-hyperglycaemic agent used to treat non-insulin–dependent diabetes mellitus with species-dependent metabolic profiles. In this study, we investigated tolbutamide metabolism in chimeric TK-NOG mice transplanted with human hepatocytes (humanised-liver mice).

2. Substantial 4-hydroxytolbutamide and 4-carboxytolbutamide production was observed in hepatocytes from humanised-liver mice (Hu-Liver cells) and humans, whereas 4-carboxytolbutamide production was not detected in mouse hepatocytes. In Hu-Liver cells, 4-hydroxytolbutamide formation was inhibited by sulfaphenazole (CYP2C9 inhibitor), whereas 4-carboxytolbutamide formation was inhibited by raloxifene/ethinyloestradiol (aldehyde oxidase inhibitor) and disulfiram (aldehyde dehydrogenase inhibitor).

3. After a single oral dose of tolbutamide (10 mg/kg), the plasma levels of 4-carboxytolbutamide and p-tolylsulfonylurea were higher in humanised-liver mice than in TK-NOG mice. Urinary excretion was the predominant route (>99% of unchanged drug and metabolites detected in excreta) of elimination in both groups. 4-Carboxytolbutamide was the most abundant metabolite in humanised-liver mouse urine, as similarly reported for humans, whereas 4-hydroxytolbutamide was predominantly excreted in TK-NOG mouse urine.

4. These results suggest that humanised-liver mice might represent a suitable animal model for studying the successive oxidative metabolism of tolbutamide by multiple drug-metabolising enzymes. Future work is warranted to study the general nature of primary alcohol metabolism using humanised-liver mice.

Keywords:

• Tolbutamide
• humanised-liver mouse
• Hu-Liver cell
• species difference
• oxidative metabolism

Acknowledgments

We thank Drs. Mamoru Ito, Yasuyuki Ohnishi, and Hidetaka Kamimura for their advice and comments and Hiroaki Kato and Yasuhiko Ando for their technical help. This work was supported in part by the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research [BINDS]) from AMED under grant number 20am0101121j0004. This work was supported in part by the METI Artificial Intelligence-based Substance Hazard Integrated Prediction System project, Japan. SU was partly supported by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research 20K06463. We thank Joe Barber Jr., PhD, from Edanz Group (https://en-author-services.edanz.com/ac) for editing a draft of this manuscript.

Disclosure of interest

The authors report no conflict of interest.